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arXiv · 2606.03837

Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?

Luc P. J. Sträter·Hazel Doughty
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Abstract

Parameter-efficient fine-tuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters, making it attractive for video understanding where annotation and computation are expensive. However, video PEFT has focused on adapting image-pretrained models, while standard PEFT methods can also be applied to video representations. These settings are rarely compared and both confine temporal reasoning to a single component of the model, leaving open how temporal context should be distributed across backbone, PEFT and probe. In this work we provide a systematic study of model adaptation strategies for video understanding. We evaluate methods across appearance-focused, motion-focused and spatially dense settings, with a particular focus on scenarios with limited data where parameter-efficiency is most beneficial. Our results provide new insights into PEFT and probing across settings and demonstrate the importance of temporal context allocation for effective video adaptation

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Luc P. J. Sträter, Hazel Doughty. 2026-06-02. Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?. https://arxiv.org/abs/2606.03837

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